# mypy: allow-untyped-defs
# Copyright (c) Meta Platforms, Inc. and affiliates
import functools
import itertools
import operator
from collections.abc import Callable, Iterable, Sequence
from typing import cast, Optional, Union

import torch
from torch._prims_common import DimsSequenceType, DimsType
from torch.distributed.tensor._collective_utils import redistribute_cost
from torch.distributed.tensor._dtensor_spec import DTensorSpec
from torch.distributed.tensor._op_schema import (
    OpSchema,
    OpSpec,
    OpStrategy,
    PlacementList,
    StrategyType,
)
from torch.distributed.tensor.device_mesh import DeviceMesh
from torch.distributed.tensor.placement_types import (
    Partial,
    Placement,
    Replicate,
    Shard,
)


def replicate_op_strategy(op_schema: OpSchema) -> StrategyType:
    """
    Fallback strategy all use Replication()
    """
    args_strategy = op_schema.args_strategy
    kwargs_strategy = op_schema.kwargs_strategy
    inputs_strategy = args_strategy + kwargs_strategy

    output_type = [str(ret.type) for ret in op_schema.op._schema.returns]
    output_len = output_type.count("Tensor")
    # TODO(zpcore): Confirm if view op can be handle properly or not. Prevent
    # handling view ops until confirmed.
    if op_schema.op.is_view:
        raise RuntimeError(
            "fallback strategy is unable to handle view ops until confirmed"
        )
    if "List[Tensor]" in output_type:
        raise RuntimeError(
            "fallback strategy is unable to handle ops with List[Tensor] output "
            "because size of the list may depend on the op's input value"
        )

    mesh = inputs_strategy[0].mesh

    dim_sharding: PlacementList = [Replicate()] * (output_len + len(inputs_strategy))
    single_dim_placement = [dim_sharding]
    return expand_to_full_mesh_op_strategy(
        mesh, op_schema, single_dim_placement, input_index=output_len
    )


def as_list(
    x: Union[list[object], object],
    # pyre-fixme[11]: Annotation `immutable_list` is not defined as a type.
) -> Union[list[object], torch.fx.immutable_collections.immutable_list]:  # type: ignore[valid-type]
    # During tracing, `aten.sum.dim_IntList` uses `immutable_list` for its args,
    # which is an object but treated as a list by the tracer. Therefore, keep
    # `immutable_list` intact here as well.
    if type(x) is list or isinstance(x, torch.fx.immutable_collections.immutable_list):
        return x
    else:
        return [x]


def normalize_dim(dim: int, ndim: int) -> int:
    return dim if dim >= 0 else dim + ndim


def normalize_dims(dims: DimsType, ndim: int) -> DimsSequenceType:
    """Normalize a dim or a sequence of dims, so that they are all positive."""
    if isinstance(dims, int):
        dims = (normalize_dim(dims, ndim),)
    elif isinstance(dims, list):
        dims = [normalize_dim(dim, ndim) for dim in dims]
    elif isinstance(dims, tuple):
        dims = tuple(normalize_dim(dim, ndim) for dim in dims)
    return dims


def prod(xs: Iterable[int]) -> int:
    return functools.reduce(operator.mul, xs, 1)


def is_tensor_shardable(
    shape: Sequence[int],
    spec: DTensorSpec,
    allow_unbacked_sharding: Optional[bool] = None,
) -> bool:
    """
    Check if the shape is shardable according to the spec.

    allow_unbacked_sharding: determines the fallback value if unbacked shapes are involved,
    and the queried shape properties are not statically known.

    e.g. when asking if u0 is shardable on num_shards, and u0 has generic bounds [0, inf],
    the behavior of allow_unbacked_sharding is:

        None: will data-dependent error
        True: assumes shardability; we return True, allowing zero-size shards at runtime when u0 < num_shards.
        False: returns False, and lower-bounding u0, e.g. torch._check(u0 >= num_shards), is needed to enable sharding.
    """
    from torch.fx.experimental.symbolic_shapes import guard_or_false, guard_or_true

    assert allow_unbacked_sharding in [None, True, False]
    guard_fn = {
        None: bool,
        True: guard_or_false,
        False: guard_or_true,
    }[allow_unbacked_sharding]

    # number of shards in each tensor dimension
    shards_map = [1] * len(shape)
    for i, placement in enumerate(spec.placements):
        if placement.is_shard():
            shard_dim = cast(Shard, placement).dim
            if shard_dim >= len(shape):
                return False
            shards_map[shard_dim] *= spec.mesh.size(i)

    for i, dim_size in enumerate(shape):
        # TODO: maybe we should determine is_shardable based on
        #       whether it's evenly sharded or not
        if shards_map[i] > 1 and guard_fn(dim_size < shards_map[i]):
            return False

    return True


def is_tensor_evenly_shardable(shape: Sequence[int], spec: DTensorSpec) -> bool:
    """Check if the shape is evenly shardable according to the spec."""
    # number of shards in each tensor dimension
    shards_map = [1] * len(shape)
    for i, placement in enumerate(spec.placements):
        if placement.is_shard():
            shard_dim = cast(Shard, placement).dim
            shards_map[shard_dim] *= spec.mesh.size(i)

    for i, dim_size in enumerate(shape):
        if shards_map[i] > 1 and (dim_size % shards_map[i] != 0):
            return False

    return True


def is_tensor_evenly_shardable_on_dim(
    shape: Sequence[int], spec: DTensorSpec, dim: int
) -> bool:
    """Check if the shape is evenly shardable according to the spec on dim."""
    dim = normalize_dim(dim, len(shape))

    num_shards = 1
    for i, placement in enumerate(spec.placements):
        if placement.is_shard():
            shard_dim = cast(Shard, placement).dim
            if shard_dim == dim:
                num_shards *= spec.mesh.size(i)

    return shape[dim] % num_shards == 0


def is_tensor_dim_sharded(spec: DTensorSpec, dim: int) -> bool:
    """Return True if tensor dim is sharded."""
    return any(p.is_shard(dim) for p in spec.placements)


def is_tensor_partial(spec: DTensorSpec) -> bool:
    """Return True if tensor is partial on the mesh."""
    return any(p.is_partial() for p in spec.placements)


def infer_broadcast_dims_map(
    common_shape: torch.Size, input_shape: torch.Size
) -> list[int]:
    # infer the broadcast dims map, where it maps from the common shape dim to the input shape dim
    # this is aligned with the broadcast semantics
    # e.g. if common_shape = [1, 2, 3, 4] and input_shape = [2, 3, 4],
    # broadcast_dims_map will be [-1, 0, 1, 2]
    # meaning that dim 0 in the output has no mapping to the input, and dim 1 in the output maps to dim 0 in the input
    common_ndim = len(common_shape)
    input_ndim = len(input_shape)
    broadcast_dims_map = [-1] * common_ndim
    for idx in range(-1, -1 - input_ndim, -1):
        if input_shape[idx] == common_shape[idx]:
            broadcast_dims_map[common_ndim + idx] = input_ndim + idx
    return broadcast_dims_map


def map_placements_after_broadcast(
    placements: tuple[Placement, ...],
    shape: torch.Size,
    broadcast_dims_map: list[int],
    partial_to_replicate: bool = False,
) -> tuple[Placement, ...]:
    """Map each placement based on the output shape after broadcast."""
    new_placements: list[Placement] = []
    for placement in placements:
        if isinstance(placement, Partial):
            if partial_to_replicate:
                # map the partial placement to replicate
                new_placements.append(Replicate())
            else:
                new_placements.append(placement)
        elif isinstance(placement, Replicate):
            new_placements.append(placement)
        else:
            assert isinstance(placement, Shard)
            shard_dim = normalize_dim(placement.dim, len(shape))
            new_shard_dim = broadcast_dims_map[shard_dim]
            if new_shard_dim != -1:
                # there's a map from the common shape shard dim to
                # the input shape shard dim before broadcasting,
                # use that instead
                new_placements.append(Shard(new_shard_dim))
            else:
                # there's no map between common shape shard dim and
                # the input shape shard dim before broadcasting,
                # in this case it means implicit broadcasting happen
                # in this dim, so we can just mark it as replicate
                # and implicit broadcast will broadcast automatically
                # to the sharded shape
                new_placements.append(Replicate())

    return tuple(new_placements)


def generate_redistribute_costs(
    src_strategy: OpStrategy, dst_spec: DTensorSpec
) -> list[float]:
    """Generates one row in the 'redistribute_costs' matrix in an OpSpec
    The length of the returned list will match the number of strategies in 'src_strategy'.

    Each value in the row is the cost of redistributing from a particular src_strategy to dst_spec.
    """
    redistribute_costs: list[float] = [
        redistribute_cost(strat.output_spec, dst_spec)
        for strat in src_strategy.strategies
    ]

    return redistribute_costs


def expand_to_full_mesh_op_strategy(
    mesh: DeviceMesh,
    op_schema: OpSchema,
    single_mesh_dim_strategies: list[PlacementList],
    *,
    input_index: int = 1,
    inplace_op: bool = False,
    is_valid_strategy_cb: Optional[
        Callable[[list[DTensorSpec], tuple[Optional[DTensorSpec], ...]], bool]
    ] = None,
) -> OpStrategy:
    """
    Convenience function to allow writing a sharding strategy considering only a single mesh dimension,
    and have it expanded combinatorically to all mesh dimensions.

    Args:
        mesh (DeviceMesh): the device mesh to expand the strategy to
        op_schema (OpSchema): the op schema
        single_mesh_dim_strategies (list[PlacementList]): the sharding strategies to expand. The outer list is over
            different strategies.  The inner PlacementList is over the outputs and inputs of the op. If input_index is 1,
            a PlacementList looks like [output_placement, input_placement1, input_placement2, ...].
        input_index: the number of outputs of the op, defaults to 1
        inplace_op: whether the op is inplace or not, defaults to False
        is_valid_strategy_cb: a callback function to filter out invalid sharding rules, defaults to None.

    Example: Let's say `my_op(tensor_x, tensor_y) - > output_tensor`  can support sharding or replicating tensor_x,
    but always requires tensor_y to be replicated.  We can specify these valid combinations ignoring mesh dims.
    Then, we can rely on `expand_to_full_mesh_op_strategy` to create every possible combination of these shardings
    over multiple mesh dimensions, filtering out any combinations that are invalid based on the actual mesh dim size.

        single_mesh_dim_strategies = [
            # first strategy: return output sharded on first dim, shard tensor_x on its first dim, replicate tensor_y
            [Shard(0), Shard(0), Replicate()]
            # second strategy: replicate output, and both inputs
            [Replicate(), Replicate(), Replicate()]
        ]
    """
    # Expand the single_mesh_dim_strategies to full mesh dim strategies.
    all_mesh_dim_strategies = [single_mesh_dim_strategies] * mesh.ndim

    strategy_combs = itertools.product(*all_mesh_dim_strategies)

    all_strategies = []
    for strategy_comb in strategy_combs:
        spec_list: list[Optional[DTensorSpec]] = []
        for specs in zip(*strategy_comb):
            if specs[0] is not None:
                # TODO: we should fill in tensor_meta here.  If nothing else, it helps the filter strategy callback
                # pyrefly: ignore [bad-argument-type]
                spec_list.append(DTensorSpec(mesh, specs))
            else:
                spec_list.append(None)

        input_specs: list[DTensorSpec] = [
            s for s in spec_list[input_index:] if isinstance(s, DTensorSpec)
        ]

        args_strategy = op_schema.args_strategy
        kwargs_strategy = op_schema.kwargs_strategy
        input_args_strategy = args_strategy + kwargs_strategy

        if len(input_specs) != len(input_args_strategy):
            raise AssertionError(
                f"input_specs({len(input_specs)}) != strategies({len(input_args_strategy)}: "
                f"{len(args_strategy)} args + {len(kwargs_strategy)} kwargs)"
            )
        self_spec = input_args_strategy[0].strategies[0].output_spec

        if inplace_op and self_spec.placements != input_specs[0].placements:
            # if it's inplace op, we would only allow the OpSpec to be added when the
            # input_spec matches the first argument's runtime sharding, otherwise we skip
            continue

        output_specs: tuple[Optional[DTensorSpec], ...]
        if input_index > 1:
            output_specs = tuple(spec_list[:input_index])
        else:
            if spec_list[0] is not None:
                output_specs = spec_list[0]  # type: ignore[assignment]
            else:
                raise RuntimeError("output spec is None")

        # check all inputs are shardable
        if not all(
            is_tensor_shardable(inp.shape, s)
            for inp, s in zip(input_args_strategy, input_specs)
        ):
            continue

        # perform additional op-specific filtering
        if is_valid_strategy_cb is not None:
            if not is_valid_strategy_cb(input_specs, output_specs):
                continue

        redistribute_cost = [
            generate_redistribute_costs(input_strategy, input_spec)
            for input_strategy, input_spec in zip(input_args_strategy, input_specs)
        ]

        strategy = OpSpec(
            output_specs=output_specs,
            input_specs=input_specs,
            redistribute_cost=redistribute_cost,
        )
        all_strategies.append(strategy)
    return OpStrategy(all_strategies)


def shift_shard_dims_after_insert(
    placements: Sequence[Placement], insert_dim: int = 0
) -> Sequence[Placement]:
    normalized_placements: list[Placement] = []
    for placement in placements:
        if isinstance(placement, Shard) and placement.dim >= insert_dim:
            normalized_placements.append(Shard(placement.dim + 1))
        else:
            normalized_placements.append(placement)
    return normalized_placements


def shift_shard_dims_after_remove(
    placements: Sequence[Placement], remove_dim: int = 0
) -> Sequence[Placement]:
    normalized_placements: list[Placement] = []
    for placement in placements:
        if isinstance(placement, Shard) and placement.dim > remove_dim:
            normalized_placements.append(Shard(placement.dim - 1))
        else:
            normalized_placements.append(placement)
    return normalized_placements
